update documents and offset initialisation for the deformable CNN

Author: vyokky

tensorlayer/layers.py

@@ -1852,7 +1852,7 @@ class DeformableConv2dLayer(Layer):
         e.g. if apply a 3*3 kernel, the number of the last dimension should be 18 (2*3*3)
     channel_multiplier : int, The number of channels to expand to.
     filter_size : tuple (height, width) for filter size.
-    strides : tuple (height, width) for strides.
+    strides : tuple (height, width) for strides. Current implementation fix to (1, 1, 1, 1)
     act : None or activation function.
     shape : list of shape
         shape of the filters, [filter_height, filter_width, in_channels, out_channels].
@@ -1867,35 +1867,49 @@ class DeformableConv2dLayer(Layer):
     name : a string or None
         An optional name to attach to this layer.
 
+    Examples
+    --------
+    >>> network = tl.layers.InputLayer(x, name='input_layer')
+    >>> offset_1 = tl.layers.Conv2dLayer(layer=network, act =act, shape=[3, 3, 3, 18], strides=[1, 1, 1, 1],padding='SAME', name='offset_layer1')
+    >>> network = tl.layers.DeformableConv2dLayer(layer=network, act=act, offset_layer=offset_1,  shape=[3, 3, 3, 32],  name='deformable_conv_2d_layer1')
+    >>> offset_2 = tl.layers.Conv2dLayer(layer=network, act = act, shape=[3, 3, 32, 18], strides=[1, 1, 1, 1], padding='SAME', name='offset_layer2')
+    >>> network = tl.layers.DeformableConv2dLayer(layer=network, act = act, offset_layer=offset_2, shape=[3, 3, 32, 64], name='deformable_conv_2d_layer2')
+
+    References
+    -----------
+    - The deformation operation was adapted from the implementation in `<https://github.com/felixlaumon/deform-conv>`_
+
     Notes
     -----------
     - The stride is fixed as (1, 1, 1, 1)
-    - `The padding is fixed as 'same'
+    - `The padding is fixed as 'SAME'
     - The current implementation is memory-inefficient, please use carefully
     """
+
     def __init__(
             self,
             layer=None,
-            offset_layer=None,
             act=tf.identity,
-            shape=[3, 3, 10, 10],
+            offset_layer=None,
+            shape=[3, 3, 1, 100],
+            name='deformable_conv_2d_layer',
             W_init=tf.truncated_normal_initializer(stddev=0.02),
             b_init=tf.constant_initializer(value=0.0),
             W_init_args={},
-            b_init_args={},
-            name='deformable_conv_2d_layer',
+            b_init_args={}
     ):
+        if tf.__version__ < "1.4":
+            raise Exception("Deformable CNN layer requires tensrflow 1.4 or higher version")
+        
         Layer.__init__(self, name=name)
         self.inputs = layer.outputs
         self.offset_layer = offset_layer
 
-        if tf.__version__ < "1.4":
-            raise Exception("Deformable CNN layer requires tensrflow 1.4 or higher version")
-
         print("  [TL] DeformableConv2dLayer %s: shape:%s, act:%s" %
               (self.name, str(shape), act.__name__))
 
         with tf.variable_scope(name) as vs:
+
             offset = self.offset_layer.outputs
             assert offset.get_shape()[-1] == 2 * shape[0] * shape[1]
 
@@ -1912,18 +1926,19 @@ def __init__(
             initial_offsets = tf.tile(initial_offsets, [input_h, input_w, 1, 1])  # initial_offsets --> (h, w, n, 2)
             initial_offsets = tf.cast(initial_offsets, 'float32')
             grid = tf.meshgrid(
-                tf.range(input_h), tf.range(input_w), indexing='ij'
-            )
+                tf.range(- int((shape[0] - 1)/2.0), int(input_h - int((shape[0] - 1)/2.0)), 1),
+                tf.range(- int((shape[1] - 1)/2.0), int(input_w - int((shape[1] - 1)/2.0)), 1), indexing='ij')
+
             grid = tf.stack(grid, axis=-1)
             grid = tf.cast(grid, 'float32')  # grid --> (h, w, 2)
             grid = tf.expand_dims(grid, 2)  # grid --> (h, w, 1, 2)
             grid = tf.tile(grid, [1, 1, kernel_n, 1])  # grid --> (h, w, n, 2)
             grid_offset = grid + initial_offsets  # grid_offset --> (h, w, n, 2)
 
-            input_deform = tf_batch_map_offsets(self.inputs, offset, grid_offset)
+            input_deform = efficient_tf_batch_map_offsets(self.inputs, offset, grid_offset)
 
             W = tf.get_variable(name='W_conv2d', shape=[1, 1, shape[0] * shape[1], shape[-2], shape[-1]],
-                                initializer=W_init, **W_init_args)
+                              initializer=W_init, **W_init_args)
             b = tf.get_variable(name='b_conv2d', shape=(shape[-1]), initializer=b_init, **b_init_args)
 
             self.outputs = tf.reshape(act(
@@ -1936,8 +1951,11 @@ def __init__(
         self.all_drop = dict(layer.all_drop)
 
         ## offset_layer
-        self.all_layers.extend(offset_layer.all_layers)
-        self.all_params.extend(offset_layer.all_params)
+        offset_params = [osparam for osparam in offset_layer.all_params if osparam not in layer.all_params]
+        offset_layers = [oslayer for oslayer in offset_layer.all_layers if oslayer not in layer.all_layers]
+
+        self.all_params.extend(offset_params)
+        self.all_layers.extend(offset_layers)
         self.all_drop.update(offset_layer.all_drop)
 
         ## this layer